Often, objects of interest, such as motor vehicles, persons or other entities are required to be tracked by remote sensing. Sensors are mounted on platforms surveying the entities which move relative to the sensor platforms enabling kinematic motion tracking, i.e., tracking based on the positional movement of the entities. The sensors create discrete images of a scene and the images are represented as a sequence of frames. In conventional kinematic approaches, entity motion is easily blocked by structures such as buildings, trees, tunnels and the like in a sensor's viewing area. Continuity of entity motion is lost across the sensed sequence of frames when an entity goes out of view of the sensor and one unique object is mistaken for two distinct objects when the entity comes back into view of the sensor.
For example, in a scenario where an aerial sensor is capturing frames of a motor vehicle travelling on a highway, kinematic tracking allows the vehicle to be tracked accurately until the vehicle becomes occluded, e.g., enters a tunnel. Once the vehicle emerges from the tunnel, the aerial sensor has no way of correlating the vehicle to the previous view of the same vehicle, and will therefore classify the vehicle exiting the tunnel as new entity. This problem is worsened if several vehicles are entering and emerging from the tunnel. The aerial camera effectively loses tracking for all vehicles when they enter and exit a tunnel, or vehicles are obstructed from view by any structure for a few frames. The loss of tracking across the sequence of frames results in incomplete tracking of entities, and important targets are lost. The loss of tracking the motion is more pronounced when a vehicle is obscured for long periods of time. For shorter periods of occlusion, a kinematic motion tracker may be able to compensate for small frame to frame losses, but for long periods of occlusion in a sequence of frames, the kinematic motion tracker is not able to correlate two seemingly distinct entities sensed at different periods of the tracking as being the same entity.
In order to surmount these difficulties imposed by kinematic motion tracking, hyperspectral (HS) based remote sensing is often used as a substitute technology. HS imaging collects and processes the wavelength of responses of incident surfaces being exposed to a plurality of regions of the electromagnetic spectrum. HS imaging divides the spectrum into many more bands than visible light. Vehicles, persons and other entities often leave a hyperspectral “fingerprint” known as spectral profiles or spectral signatures due to their paint material, clothing material and the like. Due to the number of HS bands, algorithms are available to identify nearly any material type. A sensor capable of HS imaging captures several frames of spectral profiles and compares the profiles in each frame to track an entity without relying on motion. For each frame, the HS sensor senses multiple HS bands per pixel, creating a three-dimensional HS data cube for processing and analysis. These cubes can be compared with cubes for other frames to perform tracking. In contrast to kinematic tracking, HS imaging can “pick up” tracks of an entity hours after the entity was initially obstructed and correlate the two tracks to one object.
However, often in HS sensing, entities are represented by only a few pixels out of the entire frame relative to the number of spectral bands sensed by the HS sensor, i.e., the entity has a “rare sample size” and thus is difficult to track because statistical methods have not proved reliable on rare sample sizes. For example, a vehicle is made of metallic red paint, but that paint appears in only seven pixels out of a five megapixel image. Conventional statistical hypothesis tests, which are used to perform HS tracking; cannot be implemented on such a small sample of pixels relative to the number of HS bands sensed.
Therefore, there is a need in the at for a method and apparatus for tracking entities of small sample size via hyperspectral imaging.